Sunday, October 30, 2016

Early voters favour the Coalition (redux)

In response to my post last weekend, @damonism identified a another way of looking at early voting for the elections in 2010, 2013 and 2016.

Since the 2010 election, pre-poll voting has been made easier. A new category of pre-poll vote was created for the 2010 election: the ordinary pre-poll vote, which is made within a voter's normal electorate. These ordinary pre-poll votes are counted on election night. Declaration pre-poll votes - which since 2010 are cast outside of a person's home electorate - are counted after the election night.

Prior to 2010, all pre-poll votes were treated as declaration pre-poll votes. Pre-poll voters had to provide a good reason to pre-poll (such as living too far from a polling booth). Since 2010, pre-poll voters simply need to declare that they won't be able to make it to a polling booth on election day.

In this analysis, I compare the vote outcomes for each seat and each vote-type with the on-election-day ordinary votes. Similar conclusions hold ...
  • Absent voters in a seat typically do not favour the Coalition (when compared with on-election-day ordinary voters)
  • Postal voters in a seat typically favour the Coalition (when compared with on-election-day ordinary voters)
  • Pre-poll ordinary voters in a seat typically favour the Coalition (when compared with on-election-day ordinary voters)
  • Pre-poll declaration voters in a seat typically favour the Coalition (when compared with on-election-day ordinary voters)
  • Provisional voters in a seat do not typically favour the Coalition (when compared with on-election-day ordinary voters)

The charts, this time sorted by vote type, follow ...

Absent Votes

Postal Votes

Pre-poll Ordinary Votes

Pre-poll Declaration Votes

Provisional Votes

Total Votes

Counts and Proportions by Vote Type


For the curious (and to help ensure I have not made a doozy of an error), my python code for the above charts follows. The data comes straight from the Australian Electoral Commission.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt'../bin/markgraph.mplstyle')

# --- get data
e2016 = pd.read_csv('./Data/HouseTcpByCandidateByVoteTypeDownload-20499.csv', 
    header=1, index_col=None, quotechar='"',sep=',', na_values = ['na', '-', '.', ''])
e2013 = pd.read_csv('./Data/HouseTcpByCandidateByVoteTypeDownload-17496.csv', 
    header=1, index_col=None, quotechar='"',sep=',', na_values = ['na', '-', '.', ''])
e2010 = pd.read_csv('./Data/HouseTcpByCandidateByVoteTypeDownload-15508.csv', 
    header=1, index_col=None, quotechar='"',sep=',', na_values = ['na', '-', '.', ''])

o2016 = pd.read_csv('./Data/HouseTcpByCandidateByPollingPlaceDownload-20499.csv', 
    header=1, index_col=None, quotechar='"',sep=',', na_values = ['na', '-', '.', ''])
o2013 = pd.read_csv('./Data/HouseTcpByCandidateByPollingPlaceDownload-17496.csv', 
    header=1, index_col=None, quotechar='"',sep=',', na_values = ['na', '-', '.', ''])
o2010 = pd.read_csv('./Data/HouseTcpByCandidateByPollingPlaceDownload-15508.csv', 
    header=1, index_col=None, quotechar='"',sep=',', na_values = ['na', '-', '.', ''])

# --- some useful frames
years = [2010,  2013,  2016]
data_year_all = zip([e2010, e2013, e2016], [o2010, o2013, o2016], years)

vote_types = ['OrdinaryVotesAdj', 'AbsentVotes', 'ProvisionalVotes', 
    'PrepollOrdinaryVotes', 'PrePollVotes', 'PostalVotes','TotalVotes']
vote_names = ['Ordinary Votes', 'Absent Votes', 'Provisional Votes', 
    'Pre-poll Ordinary Votes', 'Pre-poll Declaration Votes', 'Postal Votes','Total Votes']
type_names = zip(vote_types, vote_names)

Coalition = ['LP', 'NP', 'CLP', 'LNQ', 'LNP']
Labor = ['ALP']
Other = ['IND', 'GRN', 'KAP', 'ON', 'XEN', 'PUP']

# --- now let's calculate and plot the comparisons
totals = pd.DataFrame()
for all, ords, year in data_year_all:

    all = all.copy() # let's be non-destructive
    ords = ords.copy() # let's be non-destructive
    # re-label parties
    def grouper(df):
        df['PartyGroup'] = None
        df['PartyGroup'] = df['PartyGroup'].where(~df['PartyAb'].isin(Coalition), 'Coalition')
        df['PartyGroup'] = df['PartyGroup'].where(~df['PartyAb'].isin(Labor), 'Labor')
        df['PartyGroup'] = df['PartyGroup'].where(~df['PartyAb'].isin(Other), 'Other')
    all = grouper(all)
    ords = grouper(ords)

    # check ordinary vote sums
    assert(all['OrdinaryVotes'].sum() == ords['OrdinaryVotes'].sum())

    # find the ordinary pre-poll vote totals
    indexer = ['DivisionNm', 'PartyGroup']
    prepoll = ords[ords['PollingPlace'].str.contains('PPVC|PREPOLL')]
    prepoll = prepoll.groupby(indexer).sum()[['OrdinaryVotes']] # return df
    prepoll.columns = ['PrepollOrdinaryVotes']
    # index all to match the ordinary prepoll votes DataFrame
    all = all.set_index(indexer)
    # and joint them up on index ...
    all['PrepollOrdinaryVotes'] = prepoll['PrepollOrdinaryVotes']
    # and correct ordinary votes to account for ordinary pre-poll votes
    all['OrdinaryVotesAdj'] = all['OrdinaryVotes'] - all['PrepollOrdinaryVotes']
    all = all[vote_types]

    # check row additions
    assert((all['TotalVotes'] == all[vote_types[:-1]].sum(axis=1)).all())
    # calculate vote counts
    total_votes = pd.DataFrame(all.sum()).T
    total_votes.index = [year]
    totals = totals.append(total_votes)
    # convert to percent
    allPercent = all / all.groupby(level=[0]).sum() * 100.0

    # let's focus on Coalition seats only
    allPercent = allPercent[allPercent.index.get_level_values('PartyGroup') == 'Coalition']
    # weed out Nat vs Lib contests - as these Coalition v Coalition contests confound
    allPercent['index'] = allPercent.index
    allPercent= allPercent.drop_duplicates(subset='index', keep=False)
    # and plot ...
    for type, name in zip(vote_types[1:],vote_names[1:]):
        allPercent[type+'-Ordinary'] = allPercent[type] - allPercent['OrdinaryVotesAdj']
        ax = allPercent[type+'-Ordinary'].hist(bins=25)
        ax.set_title(str(year)+' Coalition Bias in '+name+' cf Ordinary Votes')
        ax.set_xlabel('Coalition bias in percentage points') 
        ax.set_ylabel('Number of Seats')
        ax.axvline(0, color='#999999', linewidth=0.5)
        fig = ax.figure
        fig.text(0.99, 0.01, '', ha='right', va='bottom',
            fontsize='x-small', fontstyle='italic', color='#999999')

        fig.savefig("./graphs/TCP2_Coalition_"+str(year)+'_hist_'+type+'-ordinary.png', dpi=125)
    # identify any unusual outliers
    strange = allPercent[allPercent['TotalVotes-Ordinary'].abs() > 4.0]
    if len(strange):

# plot counts
totals = totals / 1000.0 # work in Thousands - easier to read
for col, name in zip(totals.columns, vote_names):
    ax = totals[col].plot(marker='s')
    ax.set_title('Vote count by year for '+name)
    ax.set_xlabel('Election Year') 
    ax.set_ylabel('Thousand Formal Votes')
    ax.set_xticks([2010,  2013,  2016]) 
    ax.set_xticklabels(['2010', '2013', '2016'])
    yr = ax.get_ylim()
    expansion = (yr[1] - yr[0]) * 0.02
    fig = ax.figure
    fig.text(0.99, 0.01, '', ha='right', va='bottom',
            fontsize='x-small', fontstyle='italic', color='#999999')

    fig.savefig('./graphs/TCP2_Vote_Count'+name+'.png', dpi=125)

Updated 2 November 2016

Additional text added to explain the ordinary pre-poll vote, which was introduced at the 2010 election.

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